Class MultiTaskLassoCVScikitsLearnNode

Multi-task L1/L2 Lasso with built-in cross-validation.
This node has been automatically generated by wrapping the ``sklearn.linear_model.coordinate_descent.MultiTaskLassoCV`` class
from the ``sklearn`` library. The wrapped instance can be accessed
through the ``scikits_alg`` attribute.
The optimization objective for MultiTaskLasso is::
(1 / (2 * n_samples)) * ||Y - XW||^Fro_2 + alpha * ||W||_21
Where::
||W||_21 = \sum_i \sqrt{\sum_j w_{ij}^2}
i.e. the sum of norm of each row.
Read more in the :ref:`User Guide <multi_task_lasso>`.
**Parameters**
eps : float, optional
Length of the path. ``eps=1e-3`` means that
``alpha_min / alpha_max = 1e-3``.
alphas : array-like, optional
List of alphas where to compute the models.
If not provided, set automaticlly.
n_alphas : int, optional
Number of alphas along the regularization path
fit_intercept : boolean
whether to calculate the intercept for this model. If set
to false, no intercept will be used in calculations
(e.g. data is expected to be already centered).
normalize : boolean, optional, default False
If ``True``, the regressors X will be normalized before regression.
copy_X : boolean, optional, default True
If ``True``, X will be copied; else, it may be overwritten.
max_iter : int, optional
The maximum number of iterations.
tol : float, optional
The tolerance for the optimization: if the updates are
smaller than ``tol``, the optimization code checks the
dual gap for optimality and continues until it is smaller
than ``tol``.
cv : int, cross-validation generator or an iterable, optional
Determines the cross-validation splitting strategy.
Possible inputs for cv are:
- None, to use the default 3-fold cross-validation,
- integer, to specify the number of folds.
- An object to be used as a cross-validation generator.
- An iterable yielding train/test splits.
For integer/None inputs, :class:`KFold` is used.
Refer :ref:`User Guide <cross_validation>` for the various
cross-validation strategies that can be used here.
verbose : bool or integer
Amount of verbosity.
n_jobs : integer, optional
Number of CPUs to use during the cross validation. If ``-1``, use
all the CPUs. Note that this is used only if multiple values for
l1_ratio are given.
selection : str, default 'cyclic'
If set to 'random', a random coefficient is updated every iteration
rather than looping over features sequentially by default. This
(setting to 'random') often leads to significantly faster convergence
especially when tol is higher than 1e-4.
random_state : int, RandomState instance, or None (default)
The seed of the pseudo random number generator that selects
a random feature to update. Useful only when selection is set to
'random'.
**Attributes**
``intercept_`` : array, shape (n_tasks,)
Independent term in decision function.
``coef_`` : array, shape (n_tasks, n_features)
Parameter vector (W in the cost function formula).
``alpha_`` : float
The amount of penalization chosen by cross validation
``mse_path_`` : array, shape (n_alphas, n_folds)
mean square error for the test set on each fold, varying alpha
``alphas_`` : numpy array, shape (n_alphas,)
The grid of alphas used for fitting.
``n_iter_`` : int
number of iterations run by the coordinate descent solver to reach
the specified tolerance for the optimal alpha.
See also
MultiTaskElasticNet
ElasticNetCV
MultiTaskElasticNetCV
**Notes**
The algorithm used to fit the model is coordinate descent.
To avoid unnecessary memory duplication the X argument of the fit method
should be directly passed as a Fortran-contiguous numpy array.

Multi-task L1/L2 Lasso with built-in cross-validation.
This node has been automatically generated by wrapping the ``sklearn.linear_model.coordinate_descent.MultiTaskLassoCV`` class
from the ``sklearn`` library. The wrapped instance can be accessed
through the ``scikits_alg`` attribute.
The optimization objective for MultiTaskLasso is::
(1 / (2 * n_samples)) * ||Y - XW||^Fro_2 + alpha * ||W||_21
Where::
||W||_21 = \sum_i \sqrt{\sum_j w_{ij}^2}
i.e. the sum of norm of each row.
Read more in the :ref:`User Guide <multi_task_lasso>`.
**Parameters**
eps : float, optional
Length of the path. ``eps=1e-3`` means that
``alpha_min / alpha_max = 1e-3``.
alphas : array-like, optional
List of alphas where to compute the models.
If not provided, set automaticlly.
n_alphas : int, optional
Number of alphas along the regularization path
fit_intercept : boolean
whether to calculate the intercept for this model. If set
to false, no intercept will be used in calculations
(e.g. data is expected to be already centered).
normalize : boolean, optional, default False
If ``True``, the regressors X will be normalized before regression.
copy_X : boolean, optional, default True
If ``True``, X will be copied; else, it may be overwritten.
max_iter : int, optional
The maximum number of iterations.
tol : float, optional
The tolerance for the optimization: if the updates are
smaller than ``tol``, the optimization code checks the
dual gap for optimality and continues until it is smaller
than ``tol``.
cv : int, cross-validation generator or an iterable, optional
Determines the cross-validation splitting strategy.
Possible inputs for cv are:
- None, to use the default 3-fold cross-validation,
- integer, to specify the number of folds.
- An object to be used as a cross-validation generator.
- An iterable yielding train/test splits.
For integer/None inputs, :class:`KFold` is used.
Refer :ref:`User Guide <cross_validation>` for the various
cross-validation strategies that can be used here.
verbose : bool or integer
Amount of verbosity.
n_jobs : integer, optional
Number of CPUs to use during the cross validation. If ``-1``, use
all the CPUs. Note that this is used only if multiple values for
l1_ratio are given.
selection : str, default 'cyclic'
If set to 'random', a random coefficient is updated every iteration
rather than looping over features sequentially by default. This
(setting to 'random') often leads to significantly faster convergence
especially when tol is higher than 1e-4.
random_state : int, RandomState instance, or None (default)
The seed of the pseudo random number generator that selects
a random feature to update. Useful only when selection is set to
'random'.
**Attributes**
``intercept_`` : array, shape (n_tasks,)
Independent term in decision function.
``coef_`` : array, shape (n_tasks, n_features)
Parameter vector (W in the cost function formula).
``alpha_`` : float
The amount of penalization chosen by cross validation
``mse_path_`` : array, shape (n_alphas, n_folds)
mean square error for the test set on each fold, varying alpha
``alphas_`` : numpy array, shape (n_alphas,)
The grid of alphas used for fitting.
``n_iter_`` : int
number of iterations run by the coordinate descent solver to reach
the specified tolerance for the optimal alpha.
See also
MultiTaskElasticNet
ElasticNetCV
MultiTaskElasticNetCV
**Notes**
The algorithm used to fit the model is coordinate descent.
To avoid unnecessary memory duplication the X argument of the fit method
should be directly passed as a Fortran-contiguous numpy array.

_stop_training(self,
**kwargs)

execute(self,
x)

Predict using the linear model

This node has been automatically generated by wrapping the sklearn.linear_model.coordinate_descent.MultiTaskLassoCV class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.

is_trainable()Static Method

stop_training(self,
**kwargs)

Fit linear model with coordinate descent

This node has been automatically generated by wrapping the sklearn.linear_model.coordinate_descent.MultiTaskLassoCV class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.

Fit is on grid of alphas and best alpha estimated by cross-validation.